Update README.md
Browse files
README.md
CHANGED
|
@@ -11,11 +11,11 @@ tags:
|
|
| 11 |
- spectroscopy
|
| 12 |
---
|
| 13 |
|
| 14 |
-
#
|
| 15 |
|
| 16 |
This is a specialized Large Language Model (LLM) fine-tuned for **Stellar Astrophysics**. It acts as an intelligent analytical tool that interprets raw LAMOST spectral data and provides expert-level reasoning for stellar classification.
|
| 17 |
|
| 18 |
-
##
|
| 19 |
|
| 20 |
### Model Description
|
| 21 |
The **Qwen-Stellar-classifier** moves beyond traditional "black-box" machine learning. While standard classifiers only provide a label (e.g., "G-type"), this model explains the **physics of the star**. It identifies diagnostic spectral lines and interprets them to estimate:
|
|
@@ -33,7 +33,7 @@ The **Qwen-Stellar-classifier** moves beyond traditional "black-box" machine lea
|
|
| 33 |
|
| 34 |
---
|
| 35 |
|
| 36 |
-
##
|
| 37 |
|
| 38 |
### Direct Use
|
| 39 |
This model is built for **Stellar Astrophysics enthusiasts** and researchers who need an assistant to interpret spectral data from the LAMOST telescope. It is particularly useful for explaining anomalies or verifying classifications with physical reasoning.
|
|
@@ -43,7 +43,7 @@ The model is a scientific assistant and **not a replacement** for professional p
|
|
| 43 |
|
| 44 |
---
|
| 45 |
|
| 46 |
-
##
|
| 47 |
|
| 48 |
### Training Data
|
| 49 |
The model was trained on the **LAMOST** (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) dataset. We used a curated **"Golden Dataset"** of 300 high-quality samples to ensure the model learned the specific nuances of stellar spectra.
|
|
@@ -61,7 +61,7 @@ We focused on **Stellar Astrophysics calibration**. Instead of general conversat
|
|
| 61 |
|
| 62 |
---
|
| 63 |
|
| 64 |
-
##
|
| 65 |
|
| 66 |
The model has demonstrated high accuracy in identifying stellar types, such as:
|
| 67 |
* **G-type dwarfs:** Correctly identified at temperatures near 5,500K
|
|
@@ -77,5 +77,5 @@ This is **Version 1** of the system. In the future, we plan to:
|
|
| 77 |
|
| 78 |
---
|
| 79 |
|
| 80 |
-
##
|
| 81 |
**Liyakhath Shaik** **Email:** liyakhath0409@gmail.com
|
|
|
|
| 11 |
- spectroscopy
|
| 12 |
---
|
| 13 |
|
| 14 |
+
# Qwen-Stellar-classifier
|
| 15 |
|
| 16 |
This is a specialized Large Language Model (LLM) fine-tuned for **Stellar Astrophysics**. It acts as an intelligent analytical tool that interprets raw LAMOST spectral data and provides expert-level reasoning for stellar classification.
|
| 17 |
|
| 18 |
+
## Model Details
|
| 19 |
|
| 20 |
### Model Description
|
| 21 |
The **Qwen-Stellar-classifier** moves beyond traditional "black-box" machine learning. While standard classifiers only provide a label (e.g., "G-type"), this model explains the **physics of the star**. It identifies diagnostic spectral lines and interprets them to estimate:
|
|
|
|
| 33 |
|
| 34 |
---
|
| 35 |
|
| 36 |
+
## Uses
|
| 37 |
|
| 38 |
### Direct Use
|
| 39 |
This model is built for **Stellar Astrophysics enthusiasts** and researchers who need an assistant to interpret spectral data from the LAMOST telescope. It is particularly useful for explaining anomalies or verifying classifications with physical reasoning.
|
|
|
|
| 43 |
|
| 44 |
---
|
| 45 |
|
| 46 |
+
## Training Details
|
| 47 |
|
| 48 |
### Training Data
|
| 49 |
The model was trained on the **LAMOST** (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) dataset. We used a curated **"Golden Dataset"** of 300 high-quality samples to ensure the model learned the specific nuances of stellar spectra.
|
|
|
|
| 61 |
|
| 62 |
---
|
| 63 |
|
| 64 |
+
## Evaluation Results
|
| 65 |
|
| 66 |
The model has demonstrated high accuracy in identifying stellar types, such as:
|
| 67 |
* **G-type dwarfs:** Correctly identified at temperatures near 5,500K
|
|
|
|
| 77 |
|
| 78 |
---
|
| 79 |
|
| 80 |
+
## Contact
|
| 81 |
**Liyakhath Shaik** **Email:** liyakhath0409@gmail.com
|